



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase:   str					=		{
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Optional[Any]					=		[
         'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
         'Swinv2ForImageClassification',
         'Swinv2ForMaskedImageModeling',
         'Swinv2Model',
         'Swinv2PreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swinva import (
              SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinvaForImageClassification,
              SwinvaForMaskedImageModeling,
              SwinvaModel,
              SwinvaPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase:   Dict					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




'''simple docstring'''






import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase:   Dict					=		'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase:   Union[str, Any]					=		direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
lowerCAmelCase:   Union[str, Any]					=		re.compile(r'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
lowerCAmelCase:   Optional[Any]					=		re.compile(r'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase:   int					=		re.compile(r'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase:   List[Any]					=		[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]
def  lowerCamelCase__			(		_A							):
  a		:   Optional[Any]	  =	re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'					,     _A							)
  return [m.group(0							) for m in matches]
def  lowerCamelCase__			(		):
  a		:   Union[str, Any]	  =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  a		:   Union[str, Any]	  =	{
      config.replace('Config'					,     ''							): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  a		:   Union[str, Any]	  =	collections.defaultdict(_A							)
  a		:   Optional[Any]	  =	collections.defaultdict(_A							)
  a		:   int	  =	collections.defaultdict(_A							)

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_A							):
    a		:   Optional[Any]	  =	None
    if _re_tf_models.match(_A							) is not None:
      a		:   List[str]	  =	tf_models
      a		:   str	  =	_re_tf_models.match(_A							).groups()[0]
    elif _re_flax_models.match(_A							) is not None:
      a		:   Optional[int]	  =	flax_models
      a		:   List[Any]	  =	_re_flax_models.match(_A							).groups()[0]
    elif _re_pt_models.match(_A							) is not None:
      a		:   int	  =	pt_models
      a		:   List[str]	  =	_re_pt_models.match(_A							).groups()[0]

    if lookup_dict is not None:
      while len(_A							) > 0:
        if attr_name in model_prefix_to_model_type:
          a		:   Optional[Any]	  =	True
          break
        # Try again after removing the last word in the name
        a		:   List[str]	  =	''.join(camel_case_split(_A							)[:-1]							)

  a		:   Any	  =	set(list(pt_models.keys()							) + list(tf_models.keys()							) + list(flax_models.keys()							)							)
  a		:   int	  =	list(_A							)
  all_models.sort()

  a		:   Optional[Any]	  =	{'model_type': all_models}
  a		:   List[str]	  =	[pt_models[t] for t in all_models]
  a		:   str	  =	[tf_models[t] for t in all_models]
  a		:   int	  =	[flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  a		:   Any	  =	{}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      a		:   Optional[int]	  =	'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      a		:   Union[str, Any]	  =	'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      a		:   Tuple	  =	'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      a		:   str	  =	'AutoTokenizer'

  a		:   str	  =	[processors[t] for t in all_models]

  return pd.DataFrame(_A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	[
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    a		:   Optional[Any]	  =	[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
    a		:   Optional[int]	  =	[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
    # Loop through all three frameworks
    for module, cls, mapping in zip(_A					,     _A					,     _A							):
      # The type of pipeline may not exist in this framework
      if not hasattr(_A					,     _A							):
        continue
      # First extract all model_names
      a		:   List[str]	  =	[]
      for name in getattr(_A					,     _A							).values():
        if isinstance(_A					,     _A							):
          model_names.append(_A							)
        else:
          model_names.extend(list(_A							)							)

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}							)

  return table
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   Optional[int]	  =	get_frameworks_table()
  a		:   List[str]	  =	Dataset.from_pandas(_A							)

  a		:   Optional[Any]	  =	hf_hub_download(
      'huggingface/transformers-metadata'					,     'pipeline_tags.json'					,     repo_type='dataset'					,     token=_A							)
  a		:   List[Any]	  =	Dataset.from_json(_A							)
  a		:   Optional[Any]	  =	{
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_A							)							)
  }
  a		:   Dict	  =	update_pipeline_and_auto_class_table(_A							)

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  a		:   str	  =	sorted(table.keys()							)
  a		:   List[Any]	  =	pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }							)
  a		:   Optional[Any]	  =	Dataset.from_pandas(_A							)

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_A					,     'frameworks.json'							)							)
    tags_dataset.to_json(os.path.join(_A					,     'pipeline_tags.json'							)							)

    if commit_sha is not None:
      a		:   int	  =	(
          f"""Update with commit {commit_sha}\n\nSee: """
          f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
      )
    else:
      a		:   Optional[int]	  =	'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'					,     folder_path=_A					,     repo_type='dataset'					,     token=_A					,     commit_message=_A					,     )
def  lowerCamelCase__			(		):
  a		:   int	  =	{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  a		:   List[Any]	  =	transformers_module.pipelines.SUPPORTED_TASKS
  a		:   str	  =	[]
  for key in pipeline_tasks:
    if key not in in_table:
      a		:   Dict	  =	pipeline_tasks[key]['pt']
      if isinstance(_A					,     (list, tuple)							):
        a		:   List[Any]	  =	model[0]
      a		:   Union[str, Any]	  =	model.__name__
      if model not in in_table.values():
        missing.append(_A							)

  if len(_A							) > 0:
    a		:   List[str]	  =	', '.join(_A							)
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        f"""`utils/update_metadata.py`: {msg}. Please add them!"""							)


if __name__ == "__main__":
     lowerCAmelCase:   Tuple					=		argparse.ArgumentParser()
     parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
     parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
     parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
     lowerCAmelCase:   Optional[Any]					=		parser.parse_args()

     if args.check_only:
          check_pipeline_tags()
     else:
          update_metadata(args.token, args.commit_sha)